Writing / 4 posts
AI, the people who use it, and the research
Mostly why AI projects succeed or stall inside real organizations: the experts who resist it and are often right, the bias it inherits from us, and the systems it lands in. Plus the occasional reading note on the research, with the numbers kept intact.
Aging & Brain Health: What I'm Reading (Mid-March 2026)
A quick roundup of research and news that caught my attention this week. The through-line: noninvasive signals are getting more credible as early-warning tools, large-scale real-world datasets are reshaping how we study dementia, and...
Read itThe Human Factor: How Your Smartest Employees Can Help AI Succeed (and What Leaders Need to Know)
Unlock the design of AI by drawing on your experts. Transform the resistance employees have with AI into successful user experiences.
Read itThe Uncomfortable Truth About AI Failures: What No Vendor Will Tell You (Part 1 of 5)
When implementing AI, treating your organization like a “Complex Adaptive System” is the breakthrough you’ve been missing.
Read itBlind Spots: How AI Mirrors and Magnifies Human Bias
In the rapidly evolving landscape of artificial intelligence, bias in machine learning models has emerged as a critical concern that demands our attention. Credit card fraud, medical differences between the sexes, self-driving car bias...
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